{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53079390-42fe-4eb9-8b5a-fce4b1d7a5ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib as mpl\n",
    "from scipy.stats import lognorm\n",
    "from scipy.stats import beta \n",
    "import scipy as scp\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rc('font',family = 'Times New Roman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83b022e7-9d15-4150-a49f-581437f174f9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Base_Shear(L):\n",
    "    mm = 1-((L)/(2*R))\n",
    "    thm = np.arctan(mm/(1-mm))\n",
    "    km = (2/(thm)**2)*(1-np.cos(thm))\n",
    "    Wsm = Wtot*(1-mm**2)\n",
    "    Wfm = Wtot-Wsm\n",
    "    Mm=Wsm*km*R+Wfm*R*(1-mm)\n",
    "    Vm = Mm/hi\n",
    "    return Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c411d66-f276-46d0-a150-395160ca270d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Rotation_Uplift(theta):\n",
    "    Lm2a=Ly+0.01\n",
    "    Lm2b=0\n",
    "    true = 0\n",
    "    while(true==0):\n",
    "        wm2 = theta/((2/Lm2a)-(1/(2*R)))\n",
    "        N2 = 0.55*(Lm2a-Ly)*p*((kuus/p)/(1+kuus*(Lm2a-Ly)/(A*Es)))**(1/3)\n",
    "        Lm2b = (2*wm2*N2/p)**0.5+Ly\n",
    "        if(abs(Lm2a-Lm2b)<0.001):\n",
    "            true = 1\n",
    "        else:\n",
    "            Lm2a=Lm2b\n",
    "    return wm2,Lm2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f78c414-376b-4952-bfa0-ba8633d07147",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "343.175"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = 9.805\n",
    "\n",
    "H = 16.5\n",
    "R = 13.9\n",
    "n = 8\n",
    "bw = 2*np.pi*R/n\n",
    "\n",
    "fillr = 0.95\n",
    "\n",
    "ro = 0.998\n",
    "ph = ro*g*H*fillr\n",
    "p = ph*bw\n",
    "\n",
    "tb = 6/1000\n",
    "tw = 17/1000\n",
    "A = bw*tb\n",
    "I = bw*tb**3/12\n",
    "Zp = bw*tb**2/4\n",
    "Es = 210000000\n",
    "v = 0.3 \n",
    "Fys = 235000\n",
    "My = Fys*Zp\n",
    "\n",
    "ros = 7.850 #density of steel\n",
    "Ww = ros*g*H*bw*tw #weight of wall over one side of the strip\n",
    "tr = 31/1000\n",
    "mr = 35\n",
    "Wr = mr*g #weight of roof\n",
    "Wrr = Wr/n\n",
    "mtot = ro*H*fillr*np.pi*R**2\n",
    "Wtot = mtot*g\n",
    "\n",
    "kuu = Es*bw*(tw/R)**1.5/((3*(1-v**2))**0.25)\n",
    "ktt = Es*bw*tw**2*(tw/R)**0.5/(2*(3*(1-v**2))**0.75)\n",
    "ktu = -Es*bw*tw*(tw/R)/(2*(3*(1-v**2))**0.5)\n",
    "\n",
    "kuus = kuu-ktu**2/ktt\n",
    "dtu = -ktu/(ktt*kuu-ktu**2)\n",
    "dtt = kuu/(ktt*kuu-ktu**2)\n",
    "\n",
    "Ly = (6*My/p)**0.5\n",
    "wy = p*Ly**4/(72*Es*I)\n",
    "Wr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c7fac0e-35f6-4700-85cc-74ab1a8c8ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19762961469710677"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = np.linspace(0,145,145, dtype='int')\n",
    "gamma = fillr*H/R\n",
    "vn = np.zeros(len(n))\n",
    "for i in range(len(n)):\n",
    "    vn[i] = np.pi*(2*n[i]+1)/2\n",
    "    \n",
    "I1 = np.zeros(len(n))\n",
    "I1p = np.zeros(len(n))\n",
    "\n",
    "for i in range(len(n)):\n",
    "    I1[i] = scp.special.i1(vn[i]/gamma)\n",
    "    I1p[i] = scp.special.i0(vn[i]/gamma)- scp.special.i1(vn[i]/gamma)/(vn[i]/gamma)\n",
    "\n",
    "mi = np.sum(2*mtot*gamma*(I1/(I1p*vn**3)))\n",
    "hi = H*np.sum((-1)**n*I1*(vn*(-1)**n-1)/(vn**4*I1p))/np.sum((I1/(I1p*vn**3)))\n",
    "Ci = 6.2\n",
    "ro = 998\n",
    "tw = 17.7/1000\n",
    "Es1 = Es*1000\n",
    "Ti = Ci*H*(ro/Es1)**0.5/(tw/R)**0.5\n",
    "ki = 4*np.pi**2*mi/Ti**2\n",
    "Ti\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eabc0887-5f48-452d-a25f-f18e6df29116",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.583430116180643"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lam1 = 1.8412\n",
    "mc = mtot*2*np.tanh(lam1*gamma)/(lam1*gamma*(lam1**2-1))\n",
    "hc = H*(1+(1-np.cosh(lam1*gamma))/(lam1*gamma*np.sinh(lam1*gamma)))\n",
    "Tc = 2*np.pi*(R/g)**0.5/(lam1*np.tanh(lam1*H/R))**0.5\n",
    "Tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d8fbe7a-4cac-499e-ba96-dd2540fd8728",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def GMSpectra(Tr):\n",
    "    \n",
    "    T = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]\n",
    "    Sa = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "    Sd = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "\n",
    "    for i in range(len(Sa[0,:])):\n",
    "        Sa[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "        Sd[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Displacement/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "\n",
    "    Sam = np.zeros(len(Sa[:,0]))\n",
    "    Sdm = np.zeros(len(Sa[:,0]))\n",
    "\n",
    "    for i in range(len(Sam)):\n",
    "        Sam[i] = np.median(Sa[i,:])\n",
    "        Sdm[i] = np.median(Sd[i,:])\n",
    "    return T,Sam,Sdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4527523a-971c-4543-8c62-0b512ff86cd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "T,Sam,Sdm = GMSpectra('475')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3e47889-5563-49d3-9484-3dc6e27a7fbc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute Acceleration Spectrum, $S_A$ [g]')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icon = abs(Tc-T).argmin()\n",
    "plt.plot(T,Sam)\n",
    "plt.xlim(0,6)\n",
    "plt.ylim(0,0.6)\n",
    "plt.plot((Tc,Tc),(0,Sam[icon]),color='k',ls='--')\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Absolute Acceleration Spectrum, $S_A$ [g]',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77098b65-d8ea-430b-88e8-6ef2442e29ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "586.9973229387336"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vcon = Sam[icon]*g*mc\n",
    "Vcon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6ef707d9-6bc9-46fc-83fc-00d3485eb696",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iimp = abs(Ti-T).argmin()\n",
    "plt.plot(T,Sam,color='grey',label='Median acceleration ground response spectrum')\n",
    "plt.xlim(0,7)\n",
    "plt.ylim(0,0.6)\n",
    "plt.plot(Ti,Sam[iimp],'o',color='k',label='Impulsive component')\n",
    "plt.plot(Tc,Sam[icon],'*',color='k',label='Convective component')\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Spectral Acceleration, $S_A$ [g]',fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "plt.savefig('C:/Users/rober/Documents/ROSE/PostDoc/Tanks/Paper/Figure13.tiff',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20a375c3-730d-4158-9432-7eb2c0151839",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016143349360784265"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sam[icon]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9279588e-e7a3-483e-8b16-0924bcd4eed7",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "q = 2\n",
    "Vfd = Sam[iimp]*mi*g/q+Vcon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f697764f-515e-450d-8646-7618b854ec7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15200.18072925651"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f8da52b5-bd9b-408c-8e45-79a94e815abf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_12920\\2187572073.py:3: RuntimeWarning: divide by zero encountered in divide\n",
      "  thm = np.arctan(mm/(1-mm))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15126.131358419829"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = np.linspace(0,2,200)\n",
    "iV = abs(Vfd-Base_Shear(L)).argmin()\n",
    "Base_Shear(L[iV])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4634c1c8-ee68-4f0b-a0cf-134e8fc69b3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8542713567839196"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[iV]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6f874636-2f64-4ccd-8aad-7bce91889658",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Uplift_SepL(w):\n",
    "    if(w<=wy):\n",
    "        return w*(Ly/wy)\n",
    "    else:\n",
    "        Lm2a=Ly+0.01\n",
    "        Lm2b=0\n",
    "        true = 0\n",
    "        while(true==0):\n",
    "            N2 = 0.55*(Lm2a-Ly)*p*((kuus/p)/(1+kuus*(Lm2a-Ly)/(A*Es)))**(1/3)\n",
    "            Lm2b = (2*w*N2/p)**0.5+Ly\n",
    "            if(abs(Lm2a-Lm2b)<0.0001):\n",
    "                true = 1\n",
    "            else:\n",
    "                Lm2a=Lm2b\n",
    "            #print(Lm2a)\n",
    "        return Lm2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ae581cde-dbdc-441e-81f9-07b9c1d38a97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "w = np.linspace(0.1,0.3,500)\n",
    "Lw = np.zeros(len(w))\n",
    "\n",
    "for i in range(len(Lw)):\n",
    "    Lw[i] = Uplift_SepL(w[i])\n",
    "    \n",
    "iw = abs(L[iV]-Lw).argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ab96eb0d-7b90-4f9c-a649-e21b35808f04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2062319900062503"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th = w[iw]/L[iV]-w[iw]/(2*R)\n",
    "th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "febf6d1b-a139-4083-8233-88657b8a63be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710df27c-dcb2-4942-ad39-f5faebeed37e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b75dc7d-f557-4241-b611-ecd6b47e1b78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5df1085-a2f0-45dc-ba0c-3a55210a9646",
   "metadata": {},
   "outputs": [],
   "source": []
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